Image segmentation is a branch of digital image processing that performs the task of categorizing, or classifying, the picture elements of a digital image into one or more class types. For medical imaging applications, it is common that image segmentation is performed on the voxel (volume element) of a 3-dimensional image data set with the classification types related to anatomical structure. In thoracic medical images, it is convenient to segment the image voxels into classes such as bone, lung parenchyma, soft tissue, bronchial vessels, blood vessels, etc. There are many reasons to perform such a task, such as surgical planning, treatment progress, and patient diagnosis.
Of particular interest is the image segmentation approach generally known as region growing. Starting with a seed point, i.e., a voxel position that is known to be part of a particular class type, a region of contiguous voxels is grown, or developed, about the seed point. The region growing process progresses until a stopping condition is satisfied, e.g., no more contiguous voxels are found, or a predetermined number of voxels have been visited, etc.
The research paper “Advanced navigation tools for virtual bronchoscopy” Image Processing: Algorithms and Systems III Proceedings of the SPIE, Volume 5298, pp. 147-158 (2004), by Perchet et al., describes a region growing image segmentation for tracking the anatomical airway structures in thoracic CT (computed tomography) medical images. As part of the segmentation processing, the bronchi structures are modeled by a graph, or tree based representation. The region growing method described by Perchet et al., includes a branching decision process when the algorithm encounters a bifurcation of the bronchi structure into multiple smaller bronchi structures. The original bronchi structure, or parent, splits into two or more child bronchi structures. The analysis to determine the splitting nature of the bronchi anatomy is performed on the “front propagation,” i.e., an advancing surface corresponding to the newly segmented voxels. Thus, during region growing process the parent front propagation surface propagates into two or more child surface fronts.
Pulmonary lesions typically grow within the thoracic pleural cavity. Often cancerous regions, or lesions, in the lung can be identified in CT volume images. However, segmenting the voxels in a thoracic CT image is a difficult task since the voxel values associated with the abnormal lesion tissue are generally in the same numerical range, i.e., Hounsfield units, as many normal anatomical structures, e.g. muscle, heart, and vascular tissue. A shortcoming of many approaches for segmenting pulmonary lesions is the difficulty of differentiating between the lesion and other normal anatomy structures. Often pulmonary lesions in CT images are segmented by a combination of voxel value thresholding and morphological filtering and operations as in the paper “Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images” by Kostis et al., IEEE Trans Med Imaging. 2003 October; 22(10):1259-74. The morphological filtering approach for segmenting pulmonary lesions described by Kostis et al. has difficulty distinguishing between the abnormal lesion tissue and the tissue associated with normal pulmonary structures, such as the cavity that separates the lungs. This cavity contains the heart, large blood vessels, trachea, thymus, and connective tissues.